earticle

논문검색

Diagnosis Autism by Fisher Linear Discriminant Analysis FLDA via EEG

원문정보

Mohammed J. Alhaddad, Mahmoud I. Kamel, Hussein M. Malibary, Ebtehal A. Alsaggaf, Khalid Thabit, Foud Dahlwi, Anas A. Hadi

보안공학연구지원센터(IJBSBT) International Journal of Bio-Science and Bio-Technology Vol.4 No.2 2012.06 pp.45-54
피인용수 : 0건 (자료제공 : 네이버학술정보)

초록

영어

Diagnosis of autism is one of the difficult problems facing researchers. In this paper, Electroencephalogram (EEG) based Autism diagnosis using Fisher Linear Discriminat (FLD) Analysis is presented. Multivariate analyses of all the channels (via the concatenated signals) were used. Different preprocessing techniques, different ensemble averages, as well as, different feature extraction techniques are studied. The average correct rates are (90%). Raw data features and FFT features are used. Windsor Filtered Data gave the best mean and the lower standard deviation of both raw and FFT features. Over all, FFT features have a better correct rate of 88.14% and lower standard deviation 0.0404 than raw features.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Materials and Methods
 4. Fisher Linear Discriminant Analysis
 5. Results and Discussion
 6. Conclusion
 Acknowledgments
 References

키워드

저자정보

  • Mohammed J. Alhaddad King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Mahmoud I. Kamel King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Hussein M. Malibary King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Ebtehal A. Alsaggaf King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Khalid Thabit King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Foud Dahlwi King Abdulaziz University KAU Jeddah, Saudi Arabia
  • Anas A. Hadi King Abdulaziz University KAU Jeddah, Saudi Arabia

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.